An Intelligent and Context-Aware Touring System Based on Ontology

  • Chian WangEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 850)


With the explosive development of mobile applications, users can find a tremendous amount of travel information with just a few phone taps. However, from the perspective of information quality, most applications lack a well-organized structure that can fit various user needs. On the other hand, it is also important to consider the cultural and historical characteristics of the visited sites, in addition to focusing on sightseeing. The reason is that there are often some specific relationships among the sites. For example, many temples in Taiwan are branched from a root temple. Thus, the presented information should not be limited to a single site at a single time point. A mechanism that can help users to get the desired information more effectively and efficiently is preferred. In this paper, we are going to present an intelligent and context-aware touring system by developing an active recommendation mechanism and application. We will adopt ontology to construct the temporal, spatial, and causality relationships among the tourist attractions. Then, a personalized recommendation mechanism will be developed by integrating context-aware and data mining techniques to make recommendation decisions and “push” the information to the user’s mobile devices. In this way, users can get the most appropriate information while sightseeing.


Intelligent touring system Context-aware Ontology 


  1. 1.
    Islam, A.S., Piasecki, M.: Ontology based web simulation system for hydrodynamic modeling. Simul. Model. Pract. Theor 16(7), 754–767 (2008)CrossRefGoogle Scholar
  2. 2.
    Kaza, S., Chen, H.: Evaluating ontology mapping techniques-an experiment in public safety information sharing. Decis. Support Syst. 45(4), 714–728 (2008)CrossRefGoogle Scholar
  3. 3.
    Wang, J., Ding, Z., Jiang, C.: GAOM: genetic algorithm based ontology matching. In: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (APSCC 2006), pp. 617–620 2006Google Scholar
  4. 4.
    Blanco Fernández, Y., Pazos Arias, J.J., Gil Solla, A., Ramos Cabrer, M., López Nores, M., García Duque, J., Fernández Vilas, A., Díaz Redondo, R.P., Bermejo Muñoz, J.: A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems. Knowl. - Based Syst. 21(4), 305–320 (2008)CrossRefGoogle Scholar
  5. 5.
    Li, L.H., Lee, F.M., Chan, S.C.: The blog-article recommendation system(BARS). In: Proceedings of the 2008 IAENG International Conference on Internet Computing and Web Services (ICWS), pp. 771−776 (2008)Google Scholar
  6. 6.
    Yuan, S.T., Cheng, C.: Ontology-based personalized couple clustering for heterogeneous product recommendation in mobile marketing. Expert Syst. Appl. 26(4), 461–476 (2004)CrossRefGoogle Scholar
  7. 7.
    Neches, R., Fikes, R., Finin, T., Gruber, T., Patil, R., Senator, T., Swartout, W.: Enabling technology for knowledge sharing. AI Mag. 12(3), 36–56 (1991)Google Scholar
  8. 8.
    Guarino, N., Giaretta, P.: Ontologies and knowledge bases, towards a terminological clarification. In: Knowledge Building and Knowledge Sharing, pp. 25−32 1995Google Scholar
  9. 9.
    Weng, S.S., Chang, H.L.: Using ontology network analysis for research document recommendation. Expert Syst. Appl. 34(3), 1857–1869 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Changhua University of EducationChanghuaTaiwan

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